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Article ; Online: Prospective classification of Alzheimer's disease conversion from mild cognitive impairment.

Park, Sunghong / Hong, Chang Hyung / Lee, Dong-Gi / Park, Kanghee / Shin, Hyunjung

Neural networks : the official journal of the International Neural Network Society

2023 Volume 164, Page(s) 335–344

Abstract: Alzheimer's disease (AD) is emerging as a serious problem with the rapid aging of the population, but due to the unclear cause of the disease and the absence of therapy, appropriate preventive measures are the next best thing. For this reason, it is ... ...

Abstract	Alzheimer's disease (AD) is emerging as a serious problem with the rapid aging of the population, but due to the unclear cause of the disease and the absence of therapy, appropriate preventive measures are the next best thing. For this reason, it is important to early detect whether the disease converts from mild cognitive impairment (MCI) which is a prodromal phase of AD. With the advance in brain imaging techniques, various machine learning algorithms have become able to predict the conversion from MCI to AD by learning brain atrophy patterns. However, at the time of diagnosis, it is difficult to distinguish between the conversion group and the non-conversion group of subjects because the difference between groups is small, but the within-group variability is large in brain images. After a certain period of time, the subjects of conversion group show significant brain atrophy, whereas subjects of non-conversion group show only subtle changes due to the normal aging effect. This difference on brain atrophy makes the brain images more discriminative for learning. Motivated by this, we propose a method to perform classification by projecting brain images into the future, namely prospective classification. The experiments on the Alzheimer's Disease Neuroimaging Initiative dataset show that the prospective classification outperforms ordinary classification. Moreover, the features of prospective classification indicate the brain regions that significantly influence the conversion from MCI to AD.
MeSH term(s)	Humans ; Magnetic Resonance Imaging/methods ; Alzheimer Disease/diagnostic imaging ; Prospective Studies ; Image Interpretation, Computer-Assisted/methods ; Cognitive Dysfunction/complications ; Brain/diagnostic imaging ; Atrophy/diagnostic imaging ; Atrophy/complications ; Atrophy/pathology
Language	English
Publishing date	2023-04-21
Publishing country	United States
Document type	Journal Article
ZDB-ID	740542-x
ISSN	1879-2782 ; 0893-6080
ISSN (online)	1879-2782
ISSN	0893-6080
DOI	10.1016/j.neunet.2023.04.018
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Article ; Online: Factors of quality of care and their association with smartphone based PHR adoption in South Korean hospitals.

Choi, Byung Kwan / Park, Young-Taek / Park, Hyeoun-Ae / Lane, Chris / Jo, Emmanuel C / Kang, Sunghong

BMC medical informatics and decision making

2021 Volume 21, Issue 1, Page(s) 296

Abstract: Background: Healthcare organizations have begun to adopt personal health records (PHR) systems to engage patients, but little is known about factors associated with the adoption of PHR systems at an organizational level. The objective of this study is ... ...

Abstract	Background: Healthcare organizations have begun to adopt personal health records (PHR) systems to engage patients, but little is known about factors associated with the adoption of PHR systems at an organizational level. The objective of this study is to investigate factors associated with healthcare organizations' adoption of PHR systems in South Korea. Methods: The units of analysis were hospitals with more than 100 beds. Study data of 313 hospitals were collected from May 1 to June 30, 2020. The PHR adoption status for each hospital was collected from PHR vendors and online searches. Adoption was then confirmed by downloading the hospital's PHR app and the PHR app was examined to ascertain its available functions. One major outcome variable was PHR adoption status at hospital level. Data were analysed by logistic regressions using SAS 9.4 version. Results: Out of 313 hospitals, 103 (32.9%) hospitals adopted PHR systems. The nurse-patient ratio was significantly associated with PHR adoption (OR 0.758; 0.624 to 0.920, p = 0.005). The number of health information management staff was associated with PHR adoption (OR 1.622; 1.228 to 2.141, p = 0.001). The number of CTs was positively associated with PHR adoption (OR 5.346; 1.962 to 14.568, p = 0.001). Among the hospital characteristics, the number of beds was significantly related with PHR adoption in the model of standard of nursing care (OR 1.003; 1.001 to 1.005, p < 0.001), HIM staff (OR 1.004; 1.002 to 1.006, p < 0.001), and technological infrastructure (OR 1.050; 1.003 to 1.006, p < 0.001). Conclusions: One-third of study hospitals had adopted PHR systems. Standard of nursing care as well as information technology infrastructure in terms of human resources for health information management and advanced technologies were significantly associated with adoption of PHR systems. A favourable environment for adopting new technologies in general may be associated with the adoption and use of PHR systems.
MeSH term(s)	Electronic Health Records ; Health Records, Personal ; Hospitals ; Humans ; Republic of Korea ; Smartphone
Language	English
Publishing date	2021-10-29
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2046490-3
ISSN	1472-6947 ; 1472-6947
ISSN (online)	1472-6947
ISSN	1472-6947
DOI	10.1186/s12911-021-01666-9
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Article ; Online: SIMPLEX: Multiple phase-cycled bSSFP quantitative magnetization transfer imaging with physic-guided simulation learning of neural network.

Luu, Huan Minh / Park, Sung-Hong

NeuroImage

2023 Volume 284, Page(s) 120449

Abstract: Most quantitative magnetization transfer (qMT) imaging methods require acquiring additional quantitative maps (such as ... ...

Abstract	Most quantitative magnetization transfer (qMT) imaging methods require acquiring additional quantitative maps (such as T
MeSH term(s)	Humans ; Reproducibility of Results ; Magnetic Resonance Imaging/methods ; Computer Simulation ; Neural Networks, Computer ; Image Processing, Computer-Assisted/methods
Language	English
Publishing date	2023-11-10
Publishing country	United States
Document type	Journal Article
ZDB-ID	1147767-2
ISSN	1095-9572 ; 1053-8119
ISSN (online)	1095-9572
ISSN	1053-8119
DOI	10.1016/j.neuroimage.2023.120449
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Article ; Online: SPINNED: Simulation-based physics-informed neural network for deconvolution of dynamic susceptibility contrast MRI perfusion data.

Asaduddin, Muhammad / Kim, Eung Yeop / Park, Sung-Hong

Magnetic resonance in medicine

2024

Abstract: Purpose: To propose the simulation-based physics-informed neural network for deconvolution of dynamic susceptibility contrast (DSC) MRI (SPINNED) as an alternative for more robust and accurate deconvolution compared to existing methods.: Methods: The ...

Abstract	Purpose: To propose the simulation-based physics-informed neural network for deconvolution of dynamic susceptibility contrast (DSC) MRI (SPINNED) as an alternative for more robust and accurate deconvolution compared to existing methods. Methods: The SPINNED method was developed by generating synthetic tissue residue functions and arterial input functions through mathematical simulations and by using them to create synthetic DSC MRI time series. The SPINNED model was trained using these simulated data to learn the underlying physical relation (deconvolution) between the DSC-MRI time series and the arterial input functions. The accuracy and robustness of the proposed SPINNED method were assessed by comparing it with two common deconvolution methods in DSC MRI data analysis, circulant singular value decomposition, and Volterra singular value decomposition, using both simulation data and real patient data. Results: The proposed SPINNED method was more accurate than the conventional methods across all SNR levels and showed better robustness against noise in both simulation and real patient data. The SPINNED method also showed much faster processing speed than the conventional methods. Conclusion: These results support that the proposed SPINNED method can be a good alternative to the existing methods for resolving the deconvolution problem in DSC MRI. The proposed method does not require any separate ground-truth measurement for training and offers additional benefits of quick processing time and coverage of diverse clinical scenarios. Consequently, it will contribute to more reliable, accurate, and rapid diagnoses in clinical applications compared with the previous methods including those based on supervised learning.
Language	English
Publishing date	2024-04-16
Publishing country	United States
Document type	Journal Article
ZDB-ID	605774-3
ISSN	1522-2594 ; 0740-3194
ISSN (online)	1522-2594
ISSN	0740-3194
DOI	10.1002/mrm.30095
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Article ; Online: CycleSeg: Simultaneous synthetic CT generation and unsupervised segmentation for MR-only radiotherapy treatment planning of prostate cancer.

Luu, Huan Minh / Yoo, Gyu Sang / Park, Won / Park, Sung-Hong

Medical physics

2024

Abstract: Background: MR-only radiotherapy treatment planning is an attractive alternative to conventional workflow, reducing scan time and ionizing radiation. It is crucial to derive the electron density map or synthetic CT (sCT) from MR data to perform dose ... ...

Abstract	Background: MR-only radiotherapy treatment planning is an attractive alternative to conventional workflow, reducing scan time and ionizing radiation. It is crucial to derive the electron density map or synthetic CT (sCT) from MR data to perform dose calculations to enable MR-only treatment planning. Automatic segmentation of relevant organs in MR images can accelerate the process by preventing the time-consuming manual contouring step. However, the segmentation label is available only for CT data in many cases. Purpose: We propose CycleSeg, a unified framework that generates sCT and corresponding segmentation from MR images without access to MR segmentation labels METHODS: CycleSeg utilizes the CycleGAN formulation to perform unpaired synthesis of sCT and image alignment. To enable MR (sCT) segmentation, CycleSeg incorporates unsupervised domain adaptation by using a pseudo-labeling approach with feature alignment in semantic segmentation space. In contrast to previous approaches that perform segmentation on MR data, CycleSeg could perform segmentation on both MR and sCT. Experiments were performed with data from prostate cancer patients, with 78/7/10 subjects in the training/validation/test sets, respectively. Results: CycleSeg showed the best sCT generation results, with the lowest mean absolute error of 102.2 and the lowest Fréchet inception distance of 13.0. CycleSeg also performed best on MR segmentation, with the highest average dice score of 81.0 and 81.1 for MR and sCT segmentation, respectively. Ablation experiments confirmed the contribution of the proposed components of CycleSeg. Conclusion: CycleSeg effectively synthesized CT and performed segmentation on MR images of prostate cancer patients. Thus, CycleSeg has the potential to expedite MR-only radiotherapy treatment planning, reducing the prescribed scans and manual segmentation effort, and increasing throughput.
Language	English
Publishing date	2024-02-07
Publishing country	United States
Document type	Journal Article
ZDB-ID	188780-4
ISSN	2473-4209 ; 0094-2405
ISSN (online)	2473-4209
ISSN	0094-2405
DOI	10.1002/mp.16976
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Article ; Online: Factors of quality of care and their association with smartphone based PHR adoption in South Korean hospitals

Byung Kwan Choi / Young-Taek Park / Hyeoun-Ae Park / Chris Lane / Emmanuel C. Jo / Sunghong Kang

BMC Medical Informatics and Decision Making, Vol 21, Iss 1, Pp 1-

2021 Volume 9

Abstract: Abstract Background Healthcare organizations have begun to adopt personal health records (PHR) systems to engage patients, but little is known about factors associated with the adoption of PHR systems at an organizational level. The objective of this ... ...

Abstract	Abstract Background Healthcare organizations have begun to adopt personal health records (PHR) systems to engage patients, but little is known about factors associated with the adoption of PHR systems at an organizational level. The objective of this study is to investigate factors associated with healthcare organizations’ adoption of PHR systems in South Korea. Methods The units of analysis were hospitals with more than 100 beds. Study data of 313 hospitals were collected from May 1 to June 30, 2020. The PHR adoption status for each hospital was collected from PHR vendors and online searches. Adoption was then confirmed by downloading the hospital’s PHR app and the PHR app was examined to ascertain its available functions. One major outcome variable was PHR adoption status at hospital level. Data were analysed by logistic regressions using SAS 9.4 version. Results Out of 313 hospitals, 103 (32.9%) hospitals adopted PHR systems. The nurse-patient ratio was significantly associated with PHR adoption (OR 0.758; 0.624 to 0.920, p = 0.005). The number of health information management staff was associated with PHR adoption (OR 1.622; 1.228 to 2.141, p = 0.001). The number of CTs was positively associated with PHR adoption (OR 5.346; 1.962 to 14.568, p = 0.001). Among the hospital characteristics, the number of beds was significantly related with PHR adoption in the model of standard of nursing care (OR 1.003; 1.001 to 1.005, p < 0.001), HIM staff (OR 1.004; 1.002 to 1.006, p < 0.001), and technological infrastructure (OR 1.050; 1.003 to 1.006, p < 0.001). Conclusions One-third of study hospitals had adopted PHR systems. Standard of nursing care as well as information technology infrastructure in terms of human resources for health information management and advanced technologies were significantly associated with adoption of PHR systems. A favourable environment for adopting new technologies in general may be associated with the adoption and use of PHR systems.
Keywords	Personal health records ; Electronic medical records ; Electronic health records ; Information systems ; Quality of care ; Computer applications to medicine. Medical informatics ; R858-859.7
Subject code	650
Language	English
Publishing date	2021-10-01T00:00:00Z
Publisher	BMC
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Network mirroring for drug repositioning

Sunghong Park / Dong-gi Lee / Hyunjung Shin

BMC Medical Informatics and Decision Making, Vol 17, Iss S1, Pp 1-

2017 Volume 11

Abstract: Abstract Background Although drug discoveries can provide meaningful insights and significant enhancements in pharmaceutical field, the longevity and cost that it takes can be extensive where the success rate is low. In order to circumvent the problem, ... ...

Abstract	Abstract Background Although drug discoveries can provide meaningful insights and significant enhancements in pharmaceutical field, the longevity and cost that it takes can be extensive where the success rate is low. In order to circumvent the problem, there has been increased interest in ‘Drug Repositioning’ where one searches for already approved drugs that have high potential of efficacy when applied to other diseases. To increase the success rate for drug repositioning, one considers stepwise screening and experiments based on biological reactions. Given the amount of drugs and diseases, however, the one-by-one procedure may be time consuming and expensive. Methods In this study, we propose a machine learning based approach for efficiently selecting candidate diseases and drugs. We assume that if two diseases are similar, then a drug for one disease can be effective against the other disease too. For the procedure, we first construct two disease networks; one with disease-protein association and the other with disease-drug information. If two networks are dissimilar, in a sense that the edge distribution of a disease node differ, it indicates high potential for repositioning new candidate drugs for that disease. The Kullback-Leibler divergence is employed to measure difference of connections in two constructed disease networks. Lastly, we perform repositioning of drugs to the top 20% ranked diseases. Results The results showed that F-measure of the proposed method was 0.75, outperforming 0.5 of greedy searching for the entire diseases. For the utility of the proposed method, it was applied to dementia and verified 75% accuracy for repositioned drugs assuming that there are not any known drugs to be used for dementia. Conclusion This research has novelty in that it discovers drugs with high potential of repositioning based on disease networks with the quantitative measure. Through the study, it is expected to produce profound insights for possibility of undiscovered drug repositioning.
Keywords	Drug repositioning ; Disease network ; Kullback-Leibler Divergence ; Semi-Supervised Learning ; Computer applications to medicine. Medical informatics ; R858-859.7
Subject code	006
Language	English
Publishing date	2017-05-01T00:00:00Z
Publisher	BMC
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Network mirroring for drug repositioning.

Park, Sunghong / Lee, Dong-Gi / Shin, Hyunjung

BMC medical informatics and decision making

2017 Volume 17, Issue Suppl 1, Page(s) 55

Abstract: Background: Although drug discoveries can provide meaningful insights and significant enhancements in pharmaceutical field, the longevity and cost that it takes can be extensive where the success rate is low. In order to circumvent the problem, there ... ...

Abstract	Background: Although drug discoveries can provide meaningful insights and significant enhancements in pharmaceutical field, the longevity and cost that it takes can be extensive where the success rate is low. In order to circumvent the problem, there has been increased interest in 'Drug Repositioning' where one searches for already approved drugs that have high potential of efficacy when applied to other diseases. To increase the success rate for drug repositioning, one considers stepwise screening and experiments based on biological reactions. Given the amount of drugs and diseases, however, the one-by-one procedure may be time consuming and expensive. Methods: In this study, we propose a machine learning based approach for efficiently selecting candidate diseases and drugs. We assume that if two diseases are similar, then a drug for one disease can be effective against the other disease too. For the procedure, we first construct two disease networks; one with disease-protein association and the other with disease-drug information. If two networks are dissimilar, in a sense that the edge distribution of a disease node differ, it indicates high potential for repositioning new candidate drugs for that disease. The Kullback-Leibler divergence is employed to measure difference of connections in two constructed disease networks. Lastly, we perform repositioning of drugs to the top 20% ranked diseases. Results: The results showed that F-measure of the proposed method was 0.75, outperforming 0.5 of greedy searching for the entire diseases. For the utility of the proposed method, it was applied to dementia and verified 75% accuracy for repositioned drugs assuming that there are not any known drugs to be used for dementia. Conclusion: This research has novelty in that it discovers drugs with high potential of repositioning based on disease networks with the quantitative measure. Through the study, it is expected to produce profound insights for possibility of undiscovered drug repositioning.
Language	English
Publishing date	2017-05-18
Publishing country	England
Document type	Journal Article
ISSN	1472-6947
ISSN (online)	1472-6947
DOI	10.1186/s12911-017-0449-x
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Attention fusion network with self-supervised learning for staging of osteonecrosis of the femoral head (ONFH) using multiple MR protocols.

Kim, Bomin / Lee, Geun Young / Park, Sung-Hong

Medical physics

2023 Volume 50, Issue 9, Page(s) 5528–5540

Abstract: Background: Osteonecrosis of the femoral head (ONFH) is characterized as bone cell death in the hip joint, involving a severe pain in the groin. The staging of ONFH is commonly based on Magnetic resonance imaging and computed tomography (CT), which are ... ...

Abstract	Background: Osteonecrosis of the femoral head (ONFH) is characterized as bone cell death in the hip joint, involving a severe pain in the groin. The staging of ONFH is commonly based on Magnetic resonance imaging and computed tomography (CT), which are important for establishing effective treatment plans. There have been some attempts to automate ONFH staging using deep learning, but few of them used only MR images. Purpose: To propose a deep learning model for MR-only ONFH staging, which can reduce additional cost and radiation exposure from the acquisition of CT images. Methods: We integrated information from the MR images of five different imaging protocols by a newly proposed attention fusion method, which was composed of intra-modality attention and inter-modality attention. In addition, a self-supervised learning was used to learn deep representations from a large amount of paired MR-CT dataset. The encoder part of the MR-CT translation network was used as a pretraining network for the staging, which aimed to overcome the lack of annotated data for staging. Ablation studies were performed to investigate the contributions of each proposed method. The area under the receiver operating characteristic curve (AUROC) was used to evaluate the performance of the networks. Results: Our model improved the performance of the four-way classification of the association research circulation osseous (ARCO) stage using MR images of the multiple protocols by 6.8%p in AUROC over a plain VGG network. Each proposed method increased the performance by 4.7%p (self-supervised learning) and 2.6%p (attention fusion) in AUROC, which was demonstrated by the ablation experiments. Conclusions: We have shown the feasibility of the MR-only ONFH staging by using self-supervised learning and attention fusion. A large amount of paired MR-CT data in hospitals can be used to further improve the performance of the staging, and the proposed method has potential to be used in the diagnosis of various diseases that require staging from multiple MR protocols.
MeSH term(s)	Humans ; Femur Head Necrosis/diagnostic imaging ; Femur Head Necrosis/pathology ; Femur Head Necrosis/surgery ; Femur Head ; Magnetic Resonance Imaging/methods ; Tomography, X-Ray Computed ; Supervised Machine Learning
Language	English
Publishing date	2023-03-30
Publishing country	United States
Document type	Journal Article
ZDB-ID	188780-4
ISSN	2473-4209 ; 0094-2405
ISSN (online)	2473-4209
ISSN	0094-2405
DOI	10.1002/mp.16380
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Article ; Online: Measurement of changes in cerebrospinal fluid pulsation after traumatic brain injury using echo-planar imaging-based functional MRI.

Kim, Jun-Hee / Im, Jae-Geun / Park, Sung-Hong

NMR in biomedicine

2023 Volume 37, Issue 3, Page(s) e5061

Abstract: Traumatic brain injury (TBI) is a major public health concern worldwide, with a high incidence and a significant impact on morbidity and mortality. The alteration of cerebrospinal fluid (CSF) dynamics after TBI is a well-known phenomenon; however, the ... ...

Abstract	Traumatic brain injury (TBI) is a major public health concern worldwide, with a high incidence and a significant impact on morbidity and mortality. The alteration of cerebrospinal fluid (CSF) dynamics after TBI is a well-known phenomenon; however, the underlying mechanisms and their implications for cognitive function are not fully understood. In this study, we propose a new approach to studying the alteration of CSF dynamics in TBI patients. Our approach involves using conventional echo-planar imaging-based functional MRI with no additional scan, allowing for simultaneous assessment of functional CSF dynamics and blood oxygen level-dependent-based functional brain activities. We utilized two previously suggested indices of (i) CSFpulse, and (ii) correlation between global brain activity and CSF inflow. Using CSFpulse, we demonstrated a significant decrease in CSF pulsation following TBI (p < 0.05), which was consistent with previous studies. Furthermore, we confirmed that the decrease in CSF pulsation was most prominent in the early months after TBI, which could be explained by ependymal ciliary loss, intracranial pressure increment, or aquaporin-4 dysregulation. We also observed a decreasing trend in the correlation between global brain activity and CSF inflow in TBI patients (p < 0.05). Our findings suggest that the decreased CSF pulsation after TBI could lead to the accumulation of toxic substances in the brain and an adverse effect on brain function. Further longitudinal studies with larger sample sizes, TBI biomarker data, and various demographic information are needed to investigate the association between cognitive decline and CSF dynamics after TBI. Overall, this study sheds light on the potential role of altered CSF dynamics in TBI-induced neurologic symptoms and may contribute to the development of novel therapeutic interventions.
MeSH term(s)	Humans ; Echo-Planar Imaging ; Brain Injuries, Traumatic/diagnostic imaging ; Brain Injuries ; Magnetic Resonance Imaging ; Brain/diagnostic imaging ; Cerebrospinal Fluid/diagnostic imaging ; Cerebrospinal Fluid/physiology
Language	English
Publishing date	2023-10-15
Publishing country	England
Document type	Journal Article
ZDB-ID	1000976-0
ISSN	1099-1492 ; 0952-3480
ISSN (online)	1099-1492
ISSN	0952-3480
DOI	10.1002/nbm.5061
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